CODEWITHSUNDEEP
pythonpandaspivot-tablereshapecrosstab
Gerrit
Gerrit

Reputation: 2677

Count occurrences in DataFrame

I've a Dataframe in this format:

| Department | Person | Power  | ... |
|------------|--------|--------|-----|
| ABC        | 1234   |  75    | ... |
| ABC        | 1235   |  25    | ... |
| DEF        | 1236   |  50    | ... |
| DEF        | 1237   | 100    | ... |
| DEF        | 1238   |  25    | ... |
| DEF        | 1239   |  50    | ... |

What I now want to get is the sum of occurrences for each value in the power column. How can I get this from my DataFrame?

| Department | 100 |  75 |  50 |  25 |
|------------|-----|-----|-----|-----|
| ABC        |   0 |   1 |   0 |   1 |
| DEF        |   1 |   0 |   2 |   1 |

Upvotes: 3

Views: 3192

Answers (2)

jezrael
jezrael

Reputation: 862751

You can use value_counts with sort_index, then generate DataFrame by to_frame and last transpose by T:

print (df.Power.value_counts().sort_index(ascending=False).to_frame().T)
       100  75   50   25 
Power    1    1    2    2

EDIT by comment:

You need crosstab:

print (pd.crosstab(df.Department, df.Power).sort_index(axis=1, ascending=False))
Power       100  75   50   25 
Department                    
ABC           0    1    0    1
DEF           1    0    2    1

Faster another solution with groupby and unstack:

print (df.groupby(['Department','Power'])
         .size()
         .unstack(fill_value=0)
         .sort_index(axis=1, ascending=False))

Power       100  75   50   25 
Department                    
ABC           0    1    0    1
DEF           1    0    2    1

If need groupby by columns Department and Person, add column Person to groupby to second position (thank you piRSquared):

print (df.groupby(['Department','Person', 'Power'])
         .size()
         .unstack(fill_value=0)
         .sort_index(axis=1, ascending=False))

Power              100  75   50   25 
Department Person                    
ABC        1234      0    1    0    0
           1235      0    0    0    1
DEF        1236      0    0    1    0
           1237      1    0    0    0
           1238      0    0    0    1
           1239      0    0    1    0

EDIT1 by comment:

If need add another missing values, use reindex:

print (df.groupby(['Department','Power'])
         .size()
         .unstack(fill_value=0)
         .reindex(columns=[100,75,50,25,0], fill_value=0))

Power       100  75   50   25   0  
Department                         
ABC           0    1    0    1    0
DEF           1    0    2    1    0

Upvotes: 4

kekert
kekert

Reputation: 966

or it can be done this way:

>>> df.groupby(['Department','Power']).count().unstack().fillna(0)

           Person               
Power         25   50   75   100
Department                      
ABC           1.0  0.0  1.0  0.0
DEF           1.0  2.0  0.0  1.0

Upvotes: 1

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